1) Questioning 2) Implementation 3) Research
- Having a data science mindset is a big change from how most organizations operate. Even organizations that call themselves data-driven, are often not using their data to create new insights. Instead, they use data to support what they already believe. This is a real challenge for your data science team. Your organization could think it has a data science mindset, but the team is just using data to reinforce what they already know. Anything that contradicts this knowledge is just seen as bad data.
That's why it's important for your data science team to have three areas of responsibility. Questioning, research, and implementation. It's key to making sure that the team uses data for discovery. It keeps the team from falling into the trap of just using data to support what they already know. In fact, a major benefit of data science is questioning established knowledge. It's like that old Mark Twain quote. "What gets us into trouble is not what we don't know. "It's what we know for sure that just ain't so." If your organization is relying on knowledge that's not backed up by the data, then you're likely to run into trouble.
Often this shared knowledge is right, but when it's wrong it can have lasting consequences. If your data science team stays true to its responsibilities, it can be a real benefit to your organization. So far, you've seen three common roles on a data science team. There's a Research Lead, who drives interesting questions, then there's the Data Analyst, who will work with the research lead to come up with interesting reports and insights. Finally, there's a Project Manager, who makes those insights actionable, and available to the rest of the organization.
Now, it's time to take these roles and place them into these three larger areas of responsibility. That way, you can see how the team comes together. Think of your team as having different overlapping areas of responsibility. They'll overlap like this Venn diagram. This is a diagram with circles that shows the overlap between different sets. Let's start our diagram with the Data Analyst. The primary area of responsibility for data analysts is to do research. This is a key part of the science in data science.
The data analyst will work with the research lead to come up with interesting questions. Then they'll research these questions, and represent them with a creative report, or chart. The data analyst is the foundation for the team. He works with both the project manager and research lead, he just works in different ways. Think of him as having an independent two-way relationship with both the project manager, and the research lead. He'll get inputs from the research lead in the form of interesting questions. Then he'll output the results in insights to the project manager so they can enforce learning.
Now let's look at the next circle in the Venn diagram. On the right, there's a circle for the Research Lead. Her area of responsibility is questioning. If you're thinking about this in a scientific method, then the research lead creates an interesting hypothesis. The research lead creates a cycle between themselves and the data analyst. She's asking questions and getting feedback. It's not as simple as sending an e-mail to the data analyst asking, "What do you think?" It's a collaborative process. The research lead asks questions and the data analysts give feedback to those questions based on the available data.
These two circles should overlap. It's a direct relationship between questions and research. The final circle in the Venn diagram is for the Project Manager. His area of responsibility is implementation. He needs to make sure that the team takes the data, and uses it for something actionable. He helps distribute insights to the rest of the organization. It's not an easy challenge to take an exploratory process and focus on organizational knowledge. Often on a data science team, you don't know the path to your most actionable insights.
The team will go through many dead ends before they find one interesting path. Still it's important to consider what these insights might look like when they're finally implemented. Each of these areas of responsibility is a map of what the team needs to accomplish. It will help reinforce the idea that your data science team is about exploration and discovery. They need to follow the data, even if it contradicts their well-established ideas.
Learn the holistic approach to building teams and deploying data science across disciplines. Identify the key roles and responsibilities, including research lead, data analyst, and project manager. Find out how to define areas of responsibility, foster effective communication, and build compelling reports and visualizations. Then see how to avoid the pitfalls of losing focus and arriving at false consensus. These techniques help you build highly skilled teams that produce deeper insights than you'll find from relying on data scientists alone.
- Creating a data-driven culture
- Defining team roles and areas of responsibility
- Finding wisdom in groups
- Presenting beautiful reports
- Thinking like a team
- Avoiding pitfalls